Review of Regional Research

, Volume 34, Issue 1, pp 61–90 | Cite as

Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?

  • Robert LehmannEmail author
  • Klaus Wohlrabe
Original Paper


In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral sub-components at the regional level. With an autoregressive distributed lag model we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different regional levels (international, national and regional) and additionally make usage of several forecast pooling strategies and factor models. Our results show that we are able to increase forecast accuracy of GVA for every sector and for all forecast horizons (one up to four quarters) compared to an autoregressive process. Finally, we show that sectoral forecasts contain more information in the short term (one quarter), whereas direct forecasts of total GVA are preferable in the medium (two and three quarters) and long term (four quarters).


Regional forecasting Gross value-added Forecast combination Disaggregated forecasts Factor models 

JEL Classification

C32 C52 C53 E37 R11 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Ifo InstituteDresdenGermany
  2. 2.Ifo InstituteMunichGermany

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